Triple
T8630882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CS300 |
E204397
|
entity |
| Predicate | icaoWakeTurbulenceCategory |
P83012
|
FINISHED |
| Object | medium |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: medium | Statement: [CS300, icaoWakeTurbulenceCategory, medium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: icaoWakeTurbulenceCategory Context triple: [CS300, icaoWakeTurbulenceCategory, medium]
-
A.
aircraftWakeTurbulenceCategory
chosen
Indicates the classification of an aircraft based on the strength of the wake turbulence it generates, typically used for separation and safety in air traffic control.
-
B.
airworthinessCategory
Indicates the regulatory airworthiness classification assigned to an aircraft or component, defining the standards and conditions under which it is approved to operate.
-
C.
flightRegime
Indicates the operational conditions or phase of flight under which an aircraft or aerospace vehicle is functioning (e.g., speed, altitude, and atmospheric regime).
-
D.
takeoffCharacteristic
Indicates the specific properties or conditions associated with how an entity takes off, such as its manner, performance, or requirements during takeoff.
-
E.
takeoffWeightClass
Indicates the classification of an aircraft or vehicle based on its weight at the time of takeoff.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca834b903c8190add96cc651e1a477 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5730309081909a9a0256c9bf5f8f |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc455906f8819082edd79cb4a1cf28 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:27 p.m.